Abstract
Artificial neural network tools for structural pavement evaluation have been developed to facilitate the determination of the integrity of existing flexible pavements. With the onset of the movement toward more mechanistic pavement design, such as Mechanistic Empirical Pavement Design Guide, nondestructive testing techniques play a major role to determine properties of pavement structures. Conventional methods such as backcalculating the layer properties are complex and either require a significant computational effort and/or frequent operator intervention. Studies are presented that show the power of artificial neural networks to estimate pavement layer properties and allow for capabilities in developing pavement performance curves and for estimating and monitoring remaining life.
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Abdallah, I.N., Nazarian, S. (2009). Rapid Interpretation of Nondestructive Testing Results Using Neural Networks. In: Gopalakrishnan, K., Ceylan, H., Attoh-Okine, N.O. (eds) Intelligent and Soft Computing in Infrastructure Systems Engineering. Studies in Computational Intelligence, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04586-8_1
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DOI: https://doi.org/10.1007/978-3-642-04586-8_1
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